ADAPT: AI-Driven Decentralized Adaptive Publishing Testbed

2026-04-05

AI Summary

This work proposes a novel journal management system that integrates closed-loop control with a multi-agent architecture to address persistent challenges in academic publishing, including submission overload, reviewer fatigue, inconsistent evaluations, opaque governance, and vulnerability to manipulation. The system orchestrates dynamic collaboration among authors, human and AI reviewers, and rotating editors through adaptive policies and diverse feedback mechanisms, enabling a decentralized, self-regulating, and auditable publication process. It innovatively incorporates non-permanent role assignments, human-led decision-making, data confidentiality, and dynamic governance—features that transcend the limitations of traditional centralized models. Simulation experiments demonstrate that the approach effectively mitigates manuscript backlogs, balances reviewing workloads, suppresses collusive behavior, and exhibits robust, interpretable system responsiveness.

Abstract

Scholarly publishing faces increasingly strong stressors, including submission overload, reviewer fatigue, inconsistent evaluation, governance opacity, and vulnerability to manipulation in old and new forms. While recent studies applied artificial intelligence to improve specific steps (e.g., triage, reviewer recommendation, or automated critique), they typically work under centralized editorial control and offer limited mechanisms for system-level adaptivity and auditability. Here we present ADAPT (AI-Driven Decentralized Adaptive Publishing Testbed), an agent-based environment that models journal management as a closed-loop control system rather than a fixed editorial workflow. ADAPT integrates interacting agents in various pools (authors, reviewers -- human and AI -- and rotating editors) coupled through policy-level control and diverse feedback signals. Governance adapts to backlog pressure, reviewer disagreement, paper quality drifting, and other relevant factors, while keeping human decision authority, role non-permanence, and data confidentiality. We evaluate ADAPT in a discrete-time simulation setting across multiple operational regimes, including baseline operation, submission surges, quality drift, disagreement escalation, post-publication learning, and collusion suppression. Across regimes, we quantify backlog dynamics, reviewer load, coordination activity, and management performance. The results indicate that ADAPT works under nominal and perturbed conditions, exhibits bounded and interpretable responses under stress, and mitigates clusters with embedded interventions. This feasibility demonstration suggests a promising direction of academic publishing practice, and can be extended to real-world implementations in suitable scenarios.

Problem

• scholarly publishing
• submission overload
• reviewer fatigue
• governance opacity
• manipulation vulnerability

Innovation

• decentralized publishing
• AI-driven adaptive control
• agent-based simulation
• closed-loop scholarly workflow
• dynamic governance